Computational Chemical Genomics Screening Center

CCGS Center has its research on target identification and system pharmacology for drug lead/chemical probe discovery and cell signaling mechanism studies, with over 100 publications and invention discovery patents. The innovative work was achieved by our developed GPU-accelerated cloud computing TargetHunter machine-learning programs and diseases-specific chemogenomics knowledgebase.

The platform of “Big data to knowledge” (www.CBLigand.org/CCGS) was built on our extensive knowledge and solid working experience in development of know-how technologies and application of in silico design and virtual screening, computational chembiology, medicinal chemistry optimization, and biophysics/biochemistry validation for pharmacometrics system pharmacology and translational drug discovery research.

Our recent work on Alzheimer’s disease specific chemogenomics database was on 2014 coverpage story of a top peer-reviewed ACS journal. The innovation work includes GPU-accelerated cloud computing TargetHunter program for drug target identification (www.CBLigand.org/TargetHunter) (2013 AAPS special theme issue).

Mission and Goal of CCGS Center

The overall goal of the proposed Computational Chemical Genomics Screening Center (CCGSC) is to build a research/teaching platform and collaboration services by providing new exploratory computational tools/algorithms and chemical libraries resources in a chemical genomics scale for in-silico drug design and discovery. This goal is related to, but distinct from, cheminformatics, computational biology, bioinformatics, medicinal chemistry and pharmacology.

The objective is the more rapid identification of novel drug-like molecules, so called “lead compounds,” and their associated biological targets embracing multiple early phase drug discovery processes ranging from computational target identification and validation, to in silico screening, lead modification/scaffold hopping and virtual compound library design, and to in silico ADME profiling.

CCGSC will have a mission to promote interdisciplinary research, education and training, and foster collaborations to develop state-of-the-art computational-chemical-genomics-based in-silico drug design approaches through exploration of chemical-diversity space and its relationship to biological space.

New Positions Available:

We are seeking a microbiology / biophysics Researcher at PhD, MS or equivalent levels for participating in the chemical genomics drug discovery projects. The candidate must have expertise and working experience in the purification and characterization of proteins in E. coli and baculovirus systems using LC and MS analytical and proteomic techniques. Experience with X-ray protein crystallization studies is plus. Must demonstrated ability to design, execute and analyze experiments under supervision. Good written, verbal, and interpersonal skills are needed. Salary will be commensurate with experience. Please email your CV and three references to: Prof. XQ (Sean) Xie, Dept. of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261. Terry McGuire tfm1@pitt.edu and cc to xix15@pitt.edu

We are seeking a creative, self-motivated individual (Postdoc fellow or Programmer) with excellent interpersonal, writing and problem solving skills who is interested in working in a dynamic drug discovery environment. The successful candidate will be responsible for computational, big-data type projects and network algorithm development. Applicant must have strong background in programming, program testing and deployment, and have experience in large data modeling/analysis, database administration, and graphical user interface (GUI) design. Proficiency in at least one object-oriented programming language (e.g., Java, C++ or C#), and scripting language (e.g., Python, PHP) is preferred. Experience with GPU programming and computing, web/mobile application development, big data analysis, network information security, and Web services methodologies are desired. It is also desirable to have experience in developing modeling techniques for similarity searching and docking, in silico ADMET and off-target modeling as well as broad experience in small molecule drug discovery. The applicant must have a PhD or MS degree or equivalent, and strong communication and writing skills.

We are seeking candidates for a position at the rank of assistant professor (non-tenure stream) with a background in pharmacology, biochemistry or biophysics. The successful candidate will be expected to oversee and mentor postdoctoral researchers and graduate students working on chemical biology projects in the NIH funded Computational Chemical Genomics Screening Center (http://www.cbligand.org/xielab). The candidate’s research should be focused on drug development related to osteoporosis, neurological diseases, cancer or hematopoietic stem cells, preferably by targeting GPCRs, cannabinoid CB2 receptor, p18 or p62. Candidates should have a PhD, MD, PharmD or equivalent, and have strong communication and writing skills.

Dr Xiang-Qun (Sean) Xie is an Associate Dean for Research Innovation at School of Pharmacy and a Professor of Pharmaceutical Sciences/Drug Discovery Institute, and a PI of an integrated Medicinal Chemistry Biology laboratory of CompuGroup, BioGroup and ChemGroup (www.CBLigand.org/XieLab) at University of Pittsburgh. He is a member of the Science Advisory Board to the US FDA. He is a Founding Director of Computational Chemical Genomics Screening Center (www.CBLigand.org/CCGS), and a Director/PI of NIH National Center of Excellence for CDAR (www.CDARCenter.org). He holds joint positions at Dept of Computational Biology and Dept of Structural Biology, and Pitt Cancer Institute MT/DD Program.

Dr. Xie is an Editorial Advisory Board member for AAPS Journal and American Journal of Molecular Biology, and Associate Editor of BMC Pharmacology Toxicology. He was a Charter Member of NIH BPNS Study Section, and an ad hoc expert reviewer for UK MRC foundation; the Wellcome Trust Fund; the Netherland Organization for Scientific Research Council; the Austrian Science Fund (FWF) Erwin Schrödinger Fellowship; and the Chinese Natural Science Foundation.

Dr. Xie also holds/held honorary professorship in top institutes and colleges of pharmacy in China, including Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin Stem Cell Medical Center; and Shanghai Jiaotong University. He was an invited International Assessment Panelist for Fudan University, a member of the Board of Directors of the Chinese Association of Professionals in Science and Technology, and a Chair of the CAPST-Biomedical & Pharmaceutical Society.

Dr. Xie's Team

Prof Xie and his team are known for their pioneering research for the development of internationally recognized diseases domain-specific chemogenomics knowledgebases, which is an integrated platform of "Big Data to Knowledge" computational chemogenomics-based target identification and system pharmacology for translational research. His recent work on Alzheimer's disease specific chemogenomics database (www.CBLigand.org/AD/) was on 2014 coverpage story of a top peer-reviewed ACS journal. The innovation work includes GPU-accelerated cloud computing TargetHunter program for drug target identification (www.CBLigand.org/TargetHunter) (2013 AAPS special theme issue). His lab was the first discovered/patented INK4C-targeting small molecule inhibitors for hematopoietic stem cell expansion (Nature Comm 2015), was the first discovered/patented p62ZZ chemical inhibitors for multiple myeloma (Nature Leukemia 2015), and also reported/patented novel ligands specific to cannabinoid CB2 receptor for osteoporosis and cancers. His invention discovery patents have been successfully licensed out to Biotech/Pharma. Overall, his developed integrated cloud computing knowledgebases help to bridge the knowledge gap between biology and chemistry, and to facilitate target identification, drug repurposing, and system pharmacology analyses in a chemogenomics scale for precision medicine drug discovery. As a result, he is a recipient of 2014 AAPS Award for Outstanding Research Achievements.

Center Facility

Currently, there are limited computing resources (hardware and software) to directly support computational chemical genomics and drug design research. Some resources are already available from the University Centers/Institutes. In addition, some computing facilities already exist in the participating faculty labs or in their home Departments.

Upon establishment of the CCGSC, its members will be closely connected through the gigabit backbone campus network to the faculty in the DPS, DCB, UPDDI, UPCMLD, PSC, and faculty labs where other hardware and software resources and staff support services already exist.

Research

CCGS Center has its research on target identification and system pharmacology for drug lead/chemical probe discovery and cell signaling mechanism studies with over 100 publications and invention discovery patents.

Project Description

Alzheimer’s disease (AD) is one of the most complicated progressive neurodegeneration diseases that involve many genes, proteins, and their complex interactions. No effective medicines or treatments are available yet to stop or reverse the progression of the disease due to its polygenic nature. To facilitate discovery of new AD drugs and better understand the AD neurosignaling pathways involved, we have constructed an Alzheimer’s disease domain-specific chemogenomics knowledgebase, AlzPlatform (www.cbligand.org/AD/) with cloud computing and sourcing functions.

AlzPlatform is implemented with powerful computational algorithms, including our established TargetHunter, HTDocking, and BBB Predictor for target identification and polypharmacology analysis for AD research. The platform has assembled various AD-related chemogenomics data records, including 928 genes and 320 proteins related to AD, 194 AD drugs approved or in clinical trials, and 405 188 chemicals associated with 1 023 137 records of reported bioactivities from 38 284 corresponding bioassays and 10 050 references. Furthermore, we have demonstrated the application of the AlzPlatform in three case studies for identification of multitargets and polypharmacology analysis of FDA-approved drugs and also for screening and prediction of new AD active small chemical molecules and potential novel AD drug targets by our established TargetHunter and/or HTDocking programs. The predictions were confirmed by reported bioactivity data and our in vitro experimental validation.

TargetHunter: an in silico target identification tool for predicting therapeutic potential of small organic molecules based on chemogenomic database

Project Description

Target identification of the known bioactive compounds and novel synthetic analogs is a very important research field in medicinal chemistry, biochemistry, and pharmacology. It is also a challenging and costly step towards chemical biology and phenotypic screening. In silico identification of potential biological targets for chemical compounds offers an alternative avenue for the exploration of ligand-target interactions and biochemical mechanisms, as well as for investigation of drug repurposing. Computational target fishing mines biologically annotated chemical databases and then maps compound structures into chemogenomical space in order to predict the biological targets. We summarize the recent advances and applications in computational target fishing, such as chemical similarity searching, data mining/machine learning, panel docking, and the bioactivity spectral analysis for target identification. We then described in detail a new web-based target prediction tool, TargetHunter (http://www.cbligand.org/TargetHunter). This web portal implements a novel in silico target prediction algorithm, the Targets Associated with its MOst SImilar Counterparts, by exploring the largest chemogenomical databases, ChEMBL. Prediction accuracy reached 91.1% from the top 3 guesses on a subset of high-potency compounds from the ChEMBL database, which outperformed a published algorithm, multiple-category models. TargetHunter also features an embedded geography tool, BioassayGeoMap, developed to allow the user easily to search for potential collaborators that can experimentally validate the predicted biological target(s) or off target(s). TargetHunter therefore provides a promising alternative to bridge the knowledge gap between biology and chemistry, and significantly boost the productivity of chemogenomics researchers for in silico drug design and discovery.

Project Description

Combination therapy is a popular treatment for various diseases in the clinic. Among the successful cases, Traditional Chinese Medicinal (TCM) formulae can achieve synergistic effects in therapeutics and antagonistic effects in toxicity. However, characterizing the underlying molecular synergisms for the combination of drugs remains a challenging task due to high experimental expenses and complication of multicomponent herbal medicines.

To understand the rationale of combination therapy, we investigated Sini Decoction, a well-known TCM consisting of three herbs, as a model. We applied our established diseases-specific chemogenomics databases and our systems pharmacology approach TargetHunter to explore synergistic mechanisms of Sini Decoction in the treatment of cardiovascular diseases.

(1) We constructed a cardiovascular diseases-specific chemogenomics database, including drugs, target proteins, chemicals, and associated pathways. (2) Using our implemented chemoinformatics tools, we mapped out the interaction networks between active ingredients of Sini Decoction and their targets. (3) We also in silico predicted and experimentally confirmed that the side effects can be alleviated by the combination of the components. Overall, our results demonstrated that our cardiovascular disease-specific database was successfully applied for systems pharmacology analysis of a complicated herbal formula in predicting molecular synergetic mechanisms, and led to better understanding of a combinational therapy.

Project Description

In this manuscript, we have reported a novel 2D fingerprint-based artificial neural network QSAR (FANN-QSAR) method in order to effectively predict biological activities of structurally diverse chemical ligands. Three different types of fingerprints, namely, ECFP6, FP2 and MACCS, were used in FANN-QSAR algorithm development, and FANN-QSAR models were compared to known 3D and 2D QSAR methods using five data sets previously reported. In addition, the derived models were used to predict GPCR cannabinoid ligand binding affinities using our manually curated cannabinoid ligand database containing 1699 structurally diverse compounds with reported cannabinoid receptor subtype CB(2) activities.

To demonstrate its useful applications, the established FANN-QSAR algorithm was used as a virtual screening tool to search a large NCI compound database for lead cannabinoid compounds, and we have discovered several compounds with good CB(2) binding affinities ranging from 6.70 nM to 3.75 μM. To the best of our knowledge, this is the first report for a fingerprint-based neural network approach validated with a successful virtual screening application in identifying lead compounds. The studies proved that the FANN-QSAR method is a useful approach to predict bioactivities or properties of ligands and to find novel lead compounds for drug discovery research.

Project Description

Among cyclin-dependent kinase inhibitors that control the G1 phase in cell cycle, only p18 and p27 can negatively regulate haematopoietic stem cell (HSC) self-renewal. In this manuscript, we demonstrate that p18 protein is a more potent inhibitor of HSC self-renewal than p27 in mouse models and its deficiency promoted HSC expansion in long-term culture. Single-cell analysis indicated that deleting p18 gene favoured self-renewing division of HSC in vitro. Based on the structure of p18 protein and in-silico screening, we further identified novel smallmolecule inhibitors that can specifically block the activity of p18 protein.

Our selected lead compounds were able to expand functional HSCs in a short-term culture. Thus, these putative small-molecule inhibitors for p18 protein are valuable for further dissecting the signalling pathways of stem cell self-renewal and may help develop more effective chemical agents for therapeutic expansion of HSC.

Erratum to: Computational Advances for the Development of Allosteric Modulators and Bitopic Ligands in G Protein-Coupled Receptors

Project Description

Allosteric modulators of G protein-coupled receptors (GPCRs), which target at allosteric sites, have significant advantages against the corresponding orthosteric compounds including higher selectivity, improved chemical tractability or physicochemical properties, and reduced risk of receptor oversensitization. Bitopic ligands of GPCRs target both orthosteric and allosteric sites. Bitopic ligands can improve binding affinity, enhance subtype selectivity, stabilize receptors, and reduce side effects. Discovering allosteric modulators or bitopic ligands for GPCRs has become an emerging research area, in which the design of allosteric modulators is a key step in the detection of bitopic ligands. Radioligand binding and functional assays ([35S]GTPγS and ERK1/2 phosphorylation) are used to test the effects for potential modulators or bitopic ligands. High-throughput screening (HTS) in combination with disulfide trapping and fragment-based screening are used to aid the discovery of the allosteric modulators or bitopic ligands of GPCRs. When used alone, these methods are costly and can often result in too many potential drug targets, including false positives. Alternatively, low-cost and efficient computational approaches are useful in drug discovery of novel allosteric modulators and bitopic ligands to help refine the number of targets and reduce the false-positive rates.

This review summarizes the state-of-the-art computational methods for the discovery of modulators and bitopic ligands. The challenges and opportunities for future drug discovery are also discussed.

StemCellCKB: An Integrated Stem Cell-Specific Chemogenomics KnowledgeBase for Target Identification and Systems-Pharmacology Research

Project Description

Given the capacity of self-renewal and multilineage differentiation, stem cells are promising sources for use in regenerative medicines as well as in the clinical treatment of certain hematological malignancies and degenerative diseases. Complex networks of cellular signaling pathways largely determine stem cell fate and function. Small molecules that modulate these pathways can provide important biological and pharmacological insights. However, it is still challenging to identify the specific protein targets of these compounds, to explore the changes in stem cell phenotypes induced by compound treatment and to ascertain compound mechanisms of action. To facilitate stem cell related small molecule study and provide a better understanding of the associated signaling pathways, we have constructed a comprehensive domain-specific chemogenomics resource, called StemCellCKB (http://www.cbligand.org/StemCellCKB/). This new cloud-computing platform describes the chemical molecules, genes, proteins, and signaling pathways implicated in stem cell regulation.

StemCellCKB is also implemented with web applications designed specifically to aid in the identification of stem cell relevant protein targets, including TargetHunter, a machine-learning algorithm for predicting small molecule targets based on molecular fingerprints, and HTDocking, a high-throughput docking module for target prediction and systems-pharmacology analyses. We have systematically tested StemCellCKB to verify data integrity. Target-prediction accuracy has also been validated against the reported known target/compound associations. This proof-of-concept example demonstrates that StemCellCKB can (1) accurately predict the macromolecular targets of existing stem cell modulators and (2) identify novel small molecules capable of probing stem cell signaling mechanisms, for use in systems-pharmacology studies. StemCellCKB facilitates the exploration and exchange of stem cell chemogenomics data among members of the broader research community.

Project Description

The marrow microenvironment enhances both tumor growth and bone destruction in multiple myeloma (MM) through MM cell-induced activation of multiple signaling pathways in bone marrow stromal cells (BMSC) by TNFα. We reported that sequestosome-1 (p62) acts as a signaling hub for NF-kB, MAPK and PI3K activation in BMSC of MM patients and enhances MM growth and osteoclast (OCL) formation. p62 is composed of 5 domains that are involved in protein–protein interactions required for formation of these signaling complexes, but which domain of p62 mediates increase MM growth and OCL formation is unclear. Therefore, deletion constructs of p62 that lacked each of the 5 domains (PB1, ZZ, p38, TBS or UBA) were transfected into a p62−/− stromal cell line.

We found that the ZZ domains mediated BMSC enhancement of MM cell growth, IL-6 production, VCAM-1 expression and OCL formation. Using virtual modeling of the ZZ domain, we identified 6 candidate p62-ZZ inhibitory molecules and tested them for their capacity to block enhanced MM cell growth, OCL formation, IL-6 production, and VCAM-1 expression on BMSC induced by TNFα.

Chemogenomics Knowledgebases and Tools

Chemogenomics database for Alzheimer's disease (AD) is designed and constructed to collect multiple AD related protein targets and their ligands to explore the potential pharmacology of a small molecule for anti-AD

Chemogenomics Database for Drug abuse Research is designed for facilitating data-sharing and research communities for drug abuse, including genes, proteins, small molecules and signal pathways, with online structure search functions and data analysis tools implemented

Chemogenomics database for Cardiovascular disease (CVD) is designed and constructed to collect multiple CVD related protein targets and their ligands to explore the potential pharmacology of a small molecule for anti-CVD

Chemogenomics Knowleage Base for Stem Cell (SC) Research is designed to collect multiple SC-related protein targets and their ligands to explore the potential pharmacology of a small molecule for stem cell research

BBB permeability prediction by AdaBoost and SVM combining with 4 different fingerprints were employed to predict if a compound can pass the BBB(BBB+) or can not pass the BBB(BBB-)

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Dr. Xie Group

Xiang-Qun (Sean) Xie, MD, PhD, EMBA

Associate Dean for Research Innovation and Professor of Pharmaceutical Sciences, School of Pharmacy. Director of Computational Chemical Genomics Screening (CCGS) Center. Director of NIH NIDA National Center of Excellence for Computational Chemogenomics Drug Abuse Research (CDAR)